I. Instructions and Description for Programs and Data Files

1. Talent_data_start.do is a STATA do file that accesses data from 
Project Talent, using a dictionary (TalentDictionary.dct) to convert 
the raw public use file (TAL12_00_.DAT) to Stata data format. The 
do file creates time use and demographic variables, assigns IPEDS 
codes (using talent_crosswalk.dta), and dummy variables for NSSE 
colleges (using NSSE_dum), codes college majors, and produces summary 
statistics. The data file talent_main is stored for additional use 
by Tables.do. All files needed to run talent_data_start.do are 
included. These files should be copied into a folder named 
C:\data\timeuse before the do file is run.  

2. Experiment.do is a STATA do file that accesses survey responses 
from the study time framing effects experiment (surveys.dta), 
calculates summary statistics and creates entries for Table 3. 
All data from the experiment is unrestricted and the necessary files 
are included in the folder. These files should be copied into a 
folder named C:\data\timeuse before the do file is run.

3. NLSY_building_full.do is a STATA do file that accesses [restricted] 
data from the NLSY79, cleans these data and assigns variable names 
to make them consistent with the other data sets used in the project, 
codes college majors (by calling the file NLSY_major.do), and assigns 
IPEDS codes (using the file fice_merge.dta). The data file nlsy_big3.dat 
is stored for additional use by NLSY_data_start.do.

4. NLSY_data_start.do is a STATA do file that accesses [restricted] 
data created by NLSY_building_full.do (nlsy_big3.dat), creates time use 
and demographic variables, and produces summary statistics. The 
restricted data file nlsymerge.dta is stored for additional use by 
Tables.do. Note: Restricted data file (NLSY_big3.dat) is not included 
in the folder. 

5. HERI_VariableNames.do is a STATA do file that accesses [restricted] 
data from HERI and names variables. 

6. HERI_data_start.do is a STATA do file that accesses [restricted] 
data files created by HERI_VariableNames.do. This do file creates time use 
and demographic variables, codes college majors, produces summary 
statistics, and performs regressions, providing all HERI results that appear 
in the tables. Note: Restricted data files not included in the folder. 

8. NSSE_data_start.do is a STATA do file that accesses [restricted] 
data from NSSE2003 (nsse_data.dta), codes college majors, creates 
time use and demographic variables, and produces summary statistics. 
The restricted data file ncore.dta is stored for additional use by 
Tables.do. Note: Restricted data file (nsse_data.dta) is not included 
in the folder. 

9. Tables.do is a STATA do file that accesses data files created by 
programs above. The do file uses talent_main.dta, nlsymerge.dta, 
and ncore.dta to run regressions, perform Oaxaca decompositions, 
and calculate entries for various tables. Note: Restricted data files 
nlsymerge.dta and ncore.dta are not included in the folder. 

II. Instructions For Obtaining Restricted Data 

A. Higher Education Research Insititute (HERI) data

Information about the HERI datasets and the steps necessary to obtain 
them can be found at http://www.heri.ucla.edu/. Researchers must submit 
a research proposal to gain access to the survey data. The research 
proposal should demonstrate a clear idea of the specific project.

According to the website:
�When reviewing proposals, HERI staff evaluate whether:
1.	HERI data adequately matches the proposed research project;
2.	The study design is adequate to answer the questions being asked, 
	theoretical grounding is evident, and the proposal provides 
	sufficient detail about dependent and independent variables;
3.	The proposal details the process by which the investigator will 
	acquire appropriate institutional review board approval;
4.	The intended plan specified by the investigator involves advancing 
	scholarship; and
5.	The research is conducted in a manner that minimizes conflicts 
	with other research conducted by HERI staff or other investigators 
	under previously approved projects.�

There are fees associated with access to the HERI datasets. HERI will 
only provide investigators with data files that do not contain institutional 
identifiers. In order to construct a dataset that contained a consistent 
set of schools over time we met with HERI administrators on-site at UCLA. 
HERI provided us with a list of participating colleges over time, from this 
list we were able to determine the set of colleges who participated in HERI 
in at least one of the years (1987,1988,1989), at least once during 
(1993,1994,1995) and finally the school also had to be surveyed in at 
least one of the years (2003, 2004, 2005). Once we identified the set of 
colleges HERI officials assigned each college a consistent unique identifier. 

For each college, upon our request, HERI merged in information about the 
stratification cell, geographic region, college enrollment, historically 
black college or university, Hispanic-serving institution, Carnegie 
classification code, degree of urbanization, sector of institution, highest 
degree offered, levels of degree offered, institutional control or affiliation, 
total headcount, male headcount, and female headcount.

We used the questionnaire form, available on-line, to determine which control 
variables to request.  While we requested more variables the variables that 
were used in the final analyisis were: sex, birth month, birth year, race, 
enrolment status, detailed major codes, estimated parental income, father's 
education, SAT verbal score, and the time use variables for: partying, working 
(for pay), watching tv, reading for pleasure, exercising/sports, student 
clubs/groups, volunteer work,, classes/labs and studying/homework.

We renamed the variables using the attached HERI_VariableNames.do file. In the 
end we received from HERI 9 separate data files (one for each year listed above) 
limited to the subset of colleges identified above. We used the HERI enroll 
variable to limit the analysis to full time students. After merging together all 
of the files we determined (by the Carnegie Classifications variable) that some 
of the identified colleges were not 4-year institutions and these colleges were 
removed from the analysis. We also dropped schools with fewer than 10 observations 
in a given 3-year period from the sample and finally we dropped schools that were 
only represented in one of the three-year periods. This creates the final sample 
that is used by the attached  file, HERI_data_start.do, to compute summary stats 
and perform Oaxaca decompositions.

B. National Survey of Student Engagement (NSSE) data

Information about the NSSE datasets is available at their website: 
http://nsse.iub.edu/html/researchers.cfm

There are fees associated with access to the NSSE data. To gain access to the 
survey data, researchers must submit a research proposal and complete a 
data sharing agreement with the Indiana University Center for Postsecondary 
Research.  The proposal includes the following:
1.	The purpose and research questions that guide your study.
2.	Description of the data file you propose to borrow (items, cases, years, etc.) 
3.	Other data that you propose to merge or match with the NSSE data.
4.	Expected start and end dates for the analysis.
5.	The name, title, organization, email, and phone numbers of all 
	researchers that you propose to have access to the data.
Information and codebooks for the NSSE dataset can be found at 
http://nsse.iub.edu/html/2003_inst_report.cfm. After completing the proposal, 
signing agreements, and paying fees, we received 10,000 randomly selected 
cases including all variables (excluding identifiers) from the 2003 NSSE data file. 
Institutional identifiers were removed from the file, so that neither individual 
institutions nor individual students could be identified. Institutional 
characteristics (e..g., Carnegie codes) from IPEDS 2003 had been merged 
into the file.  Our main contact was Robert Gonyea, whose contact information 
is available here: http://nsse.iub.edu/html/staff.cfm

The attached file, NSSE_data_first.do, renames variables from the raw data 
file, cleans the data, codes college majors, and calculates summary statistics. 

C. The National Longitudinal Survey of Youth Geocoded data (NLSY79)

The time use variables are located in the 1981 survey module of the NLSY79. 
For this project we needed access to the NLSY79 Geocode data. The Geocode data 
contains detailed location information about the survey participants. In 
particular we used the Geocode data to determine the FICE codes of the 
college the student attended in 1981. The FICE codes are only available in 
the Geocode data. The Geocode data is only available to users who have 
successfully completed a Geocode application and signed a confidentiality agreement 
with the U. S. Bureau of Labor Statistics.  More information about obtaining 
the NLSY 79 Geocode data can be found at http://stats.bls.gov/nls/nlsgeo79.htm 

In order to use the NLSY79 one must download the CHHR NLS Investigator Software. 
One can obtain the software at: http://aaron.chrr.ohiostate.edu/manuals/gator/

The file �NLSY_building_full.do� contains a list of all variables gathered with 
the CHHR software (both non-geocode and geocode) as well as our naming scheme 
for the variables of interest. �NLSY_building_full.do� also defines the samples 
and documents the steps used to create and clean the variables in order to make 
them consistent with the other data sets used in this project. Lastly, 
�NLSY_building_full.do� calls the file �NLSY_major.do�. The �NLSY_major.do� 
file converts the NLSY definitions of college majors into ones that are consistent 
across the datasets used in this paper.  

The Geocode data enabled us to determine the FICE codes of the colleges. We 
mapped the college FICE codes into IPEDS codes in order to add to the dataset 
information about the colleges attended (type, selectivity etc). The 
�NLSY_building_full.do� file merges in the file �fice_merge.dta" which 
contains a crosswalk between FICE codes and IPEDS codes as well as information 
about the college (type, pub-pri yr-4yr degree). Once executed, the 
�NLSY_building_full.do� generates a dataset that we have named �NLSY_big3.dta� 
and this file is used in the analysis.

